Accenture
AI- Cybersecurity Forward Deployed Engineer- FDE Senior Manager
Remote Cybersecurity Engineering role with clear candidate location fit.
PostedJul 14, 2026
Eligible countries41 accepted countries
Seniority signalLead
Work settingRemote
Accepted candidate locations
Role overview
AI- Cybersecurity Forward Deployed Engineer- FDE Senior Manager
Requirements and responsibilities
Readable role content extracted into sections for faster review.
Key Responsibilities
- Lead AI security architecture and threat modeling for production agentic deployments across complex multi-stakeholder client environments—LLM systems, multi-agent pipelines, RAG architectures, and MLOps infrastructure—owning the full security design from assessment through hardened deployment
- Deliver hands-on security engineering using agentic coding tools as the primary build environment: build AI-powered detection systems, automated threat response tooling, security assessment frameworks, and governance automation using Claude Code, Cursor, or GitHub Copilot in daily delivery practice
- Own AI-specific threat surface management at programme scale: OWASP LLM Top 10 controls, prompt injection hardening, model extraction prevention, adversarial input defences, and AI supply chain security across concurrent client workstreams
- Architect and govern AI security controls across the enterprise stack: identity and access for AI systems, data pipeline security, model serving security, and multi-system integration risk across cloud platforms (AWS, Azure, or GCP)
- Lead AI governance framework implementation: EU AI Act, NIST AI RMF, and model risk management applied to live production systems, not theoretical compliance exercises
- Shape AI reinvention security strategy for client CISO and CTO: build risk-adjusted investment cases, security architecture roadmaps, and AI governance operating models aligned to commercial outcomes
- Define and publish reusable security patterns, playbooks, and accelerators that scale across multiple client engagements and grow the Secure AI practice
- Lead architecture design sessions, threat modeling workshops, and code-with sessions with client engineering and security leadership teams
- Cybersecurity domain expertise in at least one discipline (AppSec, SecOps, IAM, cloud, GRC, or offensive security)
- Proposal and SOW development, solution shaping, and client commercial engagement
- Executive workshop facilitation and C-suite / CISO-level communication
- Structured analytical thinking and hypothesis-driven problem decomposition
- Team leadership: developing and coaching managers and consultants through delivery
Here's what you need
- Minimum of 10 years of engineering experience in production environments with a cybersecurity discipline depth in at least one area: AppSec, SecOps / detection engineering, cloud security, IAM, offensive security / penetration testing, or GRC
- Minimum 2 year of hands-on experience designing and deploying agentic AI solutions in a production environment—non-negotiable; theoretical familiarity does not qualify
- Minimum 8 years of demonstrated end-to-end security delivery ownership experience in a client-embedded or production environment; internal advisory or compliance-only roles do not qualify
- Minimum 8 years working with Cloud platform security fundamentals across at least one provider (AWS, Azure, or GCP): IAM, network security, secrets management, and AI service security configurations
- Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience
Professional Skills Requirements
- Proven ability to communicate security risk in business terms: can translate threat exposure into risk-adjusted investment rationale a CISO or CFO would act on
- People lead responsibilities: experience managing, developing, and performance-managing a team of engineers; setting individual development plans and conducting career conversations
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